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Record W7117295765 · doi:10.1016/j.aucc.2025.101500

Rehabilitation in critically ill patients with COVID-19 infection: A systematic review and meta-analysis

2025· article· en· W7117295765 on OpenAlexafffund
Julie C. Reid, Joanna S Semrau, Heather K. O'Grady, Jen Hoogenes, Jeniszka Gill, Hibaa Hasan, Sophie von Teichman, Yelena Bogdanova, Shannon McKenney, Olivia Sokol, Tania J. Pereira, Vanessa Campes Dannenberg, Christopher Farley, Jose Colleti Junior, Amal Deis, David Williamson, Margaret S. Herridge, Michelle E. Kho

Bibliographic record

VenueAustralian Critical Care · 2025
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity Health NetworkHôpital du Sacré-Cœur de MontréalMcGill UniversitySinai Health SystemUniversity of TorontoHamilton Health SciencesQueen's UniversityMcMaster UniversityUniversity of AlbertaNiagara Health SystemYork UniversitySt. Joseph’s Healthcare Hamilton
FundersMcMaster University
KeywordsCritically illRehabilitationCritical illnessMEDLINE

Abstract

fetched live from OpenAlex

INTRODUCTION: Before the pandemic, intensive care unit rehabilitation was common. However, for critically ill patients with COVID-19 infection, rehabilitation became secondary to lifesaving measures and managing scarce resources. OBJECTIVE: In this systematic review, we investigated the impact of rehabilitation for critically ill adults with COVID-19 infection on outcomes. DATA SOURCES: Five electronic databases from 2020 to 2024 were searched for this study. STUDY SELECTION: Randomised controlled trials (RCTs) and nonrandomised studies of critically ill adults with COVID-19 infection receiving in-hospital rehabilitation interventions were included in this study. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers screened titles/abstracts and full texts. Intervention types were organised into 13 categories. We assessed completeness of study reporting using the Strengthening the Reporting of Observational Studies in Epidemiology guidelines and intervention reporting using the Consensus on Exercise Reporting Template. For RCTs, we assessed risk of bias, conducted meta-analyses using random-effect models, and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation approach. MAIN OUTCOMES AND MEASURES: There were 11 prespecified outcomes including physical function and resource utilisation. RESULTS: Sixty-eight studies (n = 50 observational, 8 RCTs, 4 experimental non-RCTs, and 6 other designs) enrolling 23,630 participants met inclusion criteria. Thirty-one reported interventions; mobility was the most common activity (74% of studies). Authors used 87 outcome measures at 57 reported time points. Strengthening the Reporting of Observational Studies in Epidemiology scores were adequate with >75% items reported. Mean Consensus on Exercise Reporting Template reporting for intervention (n = 45) was moderate (54% [23%]), and that for control groups (n = 11) was poor (48% [20%]). Risk of bias was low; very-low-certainty evidence showed that multidisciplinary functional and respiratory rehabilitation and bed cycling + tilt table may result in shorter duration of mechanical ventilation (2 RCTs, n = 116, intervention = 9.1 days, control = 11.7 days; standardised mean difference: 0.44 days [95% confidence interval: -0.81 to-0.07]) and shorter hospital length of stay (three RCTs, n = 116, intervention = 17.6-days, control = 26.2-days; standardised mean difference: 2 days [95% confidence interval: -4.22 to 0.04]). CONCLUSIONS AND RELEVANCE: Based on very-low-certainty evidence, rehabilitation may lead to shorter mechanical ventilation duration and hospital length of stay. Substantial heterogeneity across interventions, outcomes, and time points limited evidence synthesis. This review may aid in planning future rehabilitation studies with critically ill patients and for future pandemics where rehabilitation will have an important role. PROSPERO REGISTRATION: CRD42023340256.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.137
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.137
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.377
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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